GitLab announced the launch of GitLab 18, including AI capabilities natively integrated into the platform and major new innovations across core DevOps, and security and compliance workflows that are available now, with further enhancements planned throughout the year.
As companies move to more agile development, automated testing and process discovery will be critical to their ability to achieve innovation and success. Nearly 90% of software development teams have adopted agile and adoption is spreading throughout the organization, including IT, where close to two-thirds of teams have adopted it.
Speed and agility are essential for business success today and DevOps is enabling businesses to build better products and services and keep pace with today's ever-evolving customer. Without the right tools for automated testing and process discovery, however, it's unlikely that a DevOps strategy will produce the desired results.
AI and ML Drive ROI in Process Discovery and Automated Testing
Process discovery provides businesses with visibility across application workflows, enabling them to prioritize the tasks that are most frequent and time-consuming. It also takes the guesswork out of not just what needs to be developed but also what needs to be tested, it also enables automated testing, which is the ability to free up resources and shift timing to enable faster time to market and more innovative development. Together, process discovery and automated testing help businesses increase productivity across the DevOps workflow.
When artificial intelligence and machine learning are applied to process discovery and automated testing, DevOps can really take off and drive significant ROI. AI enables predictive testing, which alerts businesses if a process is succeeding or failing. With ML, businesses can ascertain what and why testing failed, fix it, and run the test again. Teams can release application updates faster, thereby responding more quickly to employee, customer, and market needs while driving results to the organization's bottom line.
As businesses move forward with DevOps, testing is crucial in successfully enabling new approaches to DevOps processes, such as shift left testing, shift left security and microservices.
Shift Left Testing
Shift left testing refers to the practice of doing more testing early on in the application development cycle, meaning every time teams update code, an automated test can be run too — before it even heads to quality assurance (QA). Traditional testing happened shortly before software goes into production, leaving teams in a difficult spot if bugs are discovered. Fixing a bug at that stage runs the risk of breaking other strands of code, throwing the project into delays and costing companies potentially millions of dollars.
With automated testing, shift left testing enables teams to identify and fix bugs earlier in the process, particularly when using continuous integration/continuous delivery (CI/CD) pipelines, which enable teams to apply application changes on a continuous basis. Process discovery is driving better shift left testing and speeding up the development cycle. It's not too long before AI is introduced to this testing process to enable predictive testing.
Shift Left Security
Similar to shift left testing, shift left security is the practice of moving security testing up earlier in the application development cycle. With the acceleration of cycles, security is often a bottleneck. Shift left security moves security more to the development teams, enabling them to work with security to put the right security guardrails in place for the application.
Automated testing enables teams to detect and fix external threats to applications, speeding up development cycles and reducing time to market.
Microservices
Microservices offer an effective way to break up an application to make it independent and easier to scale so it can meet the big-scale needs of today's businesses. Microservices not only enable scalable applications but stable ones. Microservices have become popular with containerization because they eliminate the need for big virtual machines to deploy applications.
Microservices let teams test their ideas and optimize their user experiences without requiring constant maintenance. The core of the microservices model is the ability to combine discrete functions in a way that optimizes alignment with business workflows — and to make changes quickly when new workflows are required. Test automation simplifies arriving at the right workflow.
As the demands on the business increase as customer expectations continue to escalate, agile DevOps presents a unique opportunity for businesses to speed up development without sacrificing quality or security. Automated testing and process discovery elevate the promise of agile DevOps, particularly as AI and ML become more of a factor in the development promise.
Industry News
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Reply launched Silicon Shoring, a new software delivery model powered by Artificial Intelligence.
CIQ announced the tech preview launch of Rocky Linux from CIQ for AI (RLC-AI), an operating system engineered and optimized for artificial intelligence workloads.
The Linux Foundation, the nonprofit organization enabling mass innovation through open source, announced the launch of the Cybersecurity Skills Framework, a global reference guide that helps organizations identify and address critical cybersecurity competencies across a broad range of IT job families; extending beyond cybersecurity specialists.
CodeRabbit is now available on the Visual Studio Code editor.
The integration brings CodeRabbit’s AI code reviews directly into Cursor, Windsurf, and VS Code at the earliest stages of software development—inside the code editor itself—at no cost to the developers.
Chainguard announced Chainguard Libraries for Python, an index of malware-resistant Python dependencies built securely from source on SLSA L2 infrastructure.
Sysdig announced the donation of Stratoshark, the company’s open source cloud forensics tool, to the Wireshark Foundation.
Pegasystems unveiled Pega Predictable AI™ Agents that give enterprises extraordinary control and visibility as they design and deploy AI-optimized processes.
Kong announced the introduction of the Kong Event Gateway as a part of their unified API platform.
Azul and Moderne announced a technical partnership to help Java development teams identify, remove and refactor unused and dead code to improve productivity and dramatically accelerate modernization initiatives.
Parasoft has added Agentic AI capabilities to SOAtest, featuring API test planning and creation.
Zerve unveiled a multi-agent system engineered specifically for enterprise-grade data and AI development.
LambdaTest, a unified agentic AI and cloud engineering platform, has announced its partnership with MacStadium, the industry-leading private Mac cloud provider enabling enterprise macOS workloads, to accelerate its AI-native software testing by leveraging Apple Silicon.
Tricentis announced a new capability that injects Tricentis’ AI-driven testing intelligence into SAP’s integrated toolchain, part of RISE with SAP methodology.